Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.9
6.0
Journals
MAKE
Special Issues
Deep Learning in Image Analysis and Pattern Recognition, 2nd Edition
share announcement

Deep Learning in Image Analysis and Pattern Recognition, 2nd Edition

A special issue of Machine Learning and Knowledge Extraction (ISSN 2504-4990).

Deadline for manuscript submissions: 26 November 2025 | Viewed by 2429

Special Issue Editors

Dr. Xianzhi Wang

E-Mail Website
Guest Editor
School of Computer Science, University of Technology Sydney, Sydney 2007, Australia
Interests: graph mining; multimodal learning; time series analysis; recommender systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guoqing Chao

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, China
Interests: machine learning; data mining; medical informatics; bioinformatics and service computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past decade, deep learning has demonstrated state-of-the-art performances in many image processing tasks, such as image classification, object detection, object tracking, image segmentation, etc. Despite remarkable deep learning-related achievements in computer vision, many challenging tasks and scenarios that require novel methods and theories remain. For example, lightweight object detection is required in many applications, such as autonomous driving scenarios; however, the discrepancy between the speed of a machine and human eyes remains large. Fine-grained or small-object detection is another area that has seen improvement. Weakly supervised object classification or detection is an important problem since the annotation process is time-consuming, expensive and inefficient. In addition, although deep learning achievements in computer vision have been successfully applied to many areas, greater efforts should be made to ensure that this technology can better serve humans in the future.

This Special Issue requests papers detailing new advances in deep learning methods or applications in image analysis and pattern recognition. Topics of interest include, but are not limited to, the following:

  • Image classification;
  • Object detection;
  • Object tracking;
  • Image segmentation;
  • Convolutional neural networks;
  • Diffusion model;
  • Image captioning;
  • Image clustering;
  • Representation learning for images;
  • Medical image processing;
  • Remote sensing image processing;
  • 3D image building.

Dr. Xianzhi Wang
Dr. Guoqing Chao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machine Learning and Knowledge Extraction is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • image analysis
  • pattern recognition

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 4047 KiB  
Article
Dataset Dependency in CNN-Based Copy-Move Forgery Detection: A Multi-Dataset Comparative Analysis
by Potito Valle Dell’Olmo, Oleksandr Kuznetsov, Emanuele Frontoni, Marco Arnesano, Christian Napoli and Cristian Randieri
Mach. Learn. Knowl. Extr. 2025, 7(2), 54; https://doi.org/10.3390/make7020054 - 13 Jun 2025
Viewed by 439
Abstract
Convolutional neural networks (CNNs) have established themselves over time as a fundamental tool in the field of copy-move forgery detection due to their ability to effectively identify and analyze manipulated images. Unfortunately, they still represent a persistent challenge in digital image forensics, underlining [...] Read more.
Convolutional neural networks (CNNs) have established themselves over time as a fundamental tool in the field of copy-move forgery detection due to their ability to effectively identify and analyze manipulated images. Unfortunately, they still represent a persistent challenge in digital image forensics, underlining the importance of ensuring the integrity of digital visual content. In this study, we present a systematic evaluation of the performance of a convolutional neural network (CNN) specifically designed for copy-move manipulation detection, applied to three datasets widely used in the literature in the context of digital forensics: CoMoFoD, Coverage, and CASIA v2. Our experimental analysis highlighted a significant variability of the results, with an accuracy ranging from 95.90% on CoMoFoD to 27.50% on Coverage. This inhomogeneity has been attributed to specific structural factors of the datasets used, such as the sample size, the degree of imbalance between classes, and the intrinsic complexity of the manipulations. We also investigated different regularization techniques and data augmentation strategies to understand their impact on the network performance, finding that adopting the L2 penalty and reducing the learning rate led to an accuracy increase of up to 2.5% for CASIA v2, while on CoMoFoD we recorded a much more modest impact (1.3%). Similarly, we observed that data augmentation was able to improve performance on large datasets but was ineffective on smaller ones. Our results challenge the idea of universal generalizability of CNN architectures in the context of copy-move forgery detection, highlighting instead how performance is strictly dependent on the intrinsic characteristics of the dataset under consideration. Finally, we propose a series of operational recommendations for optimizing the training process, the choice of the dataset, and the definition of robust evaluation protocols aimed at guiding the development of detection systems that are more reliable and generalizable. Full article
(This article belongs to the Special Issue Deep Learning in Image Analysis and Pattern Recognition, 2nd Edition)
►▼ Show Figures

Figure 1

14 pages, 1580 KiB  
Article
Machine Learning Classification of Fossilized Pectinodon bakkeri Teeth Images: Insights into Troodontid Theropod Dinosaur Morphology
by Jacob Bahn, Germán H. Alférez and Keith Snyder
Mach. Learn. Knowl. Extr. 2025, 7(2), 45; https://doi.org/10.3390/make7020045 - 21 May 2025
Viewed by 1470
Abstract
Although the manual classification of microfossils is possible, it can become burdensome. Machine learning offers an alternative that allows for automatic classification. Our contribution is to use machine learning to develop an automated approach for classifying images of Pectinodon bakkeri teeth. This can [...] Read more.
Although the manual classification of microfossils is possible, it can become burdensome. Machine learning offers an alternative that allows for automatic classification. Our contribution is to use machine learning to develop an automated approach for classifying images of Pectinodon bakkeri teeth. This can be expanded for use with many other species. Our approach is composed of two steps. First, PCA and K-means were applied to a numerical dataset with 459 samples collected at the Hanson Ranch Bonebed in eastern Wyoming, containing the following features: crown height, fore-aft basal length, basal width, anterior denticles, and posterior denticles per millimeter. The results obtained in this step were used to automatically organize the P. bakkeri images from two out of three clusters generated. Finally, the tooth images were used to train a convolutional neural network with two classes. The model has an accuracy of 71%, a precision of 71%, a recall of 70.5%, and an F1-score of 70.5%. Full article
(This article belongs to the Special Issue Deep Learning in Image Analysis and Pattern Recognition, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop